SoroushVahidi/scidocs huggingface dataset:
THE THREE-DIMENSIONAL EXTENSION OF SPACE SYNTAX 048
(2007)

The purpose of this study is to develop a new space syntax technique by adding third dimension in analysis of urban structure that is important to understand the formation of the cities. The theoretical framework of the study is put forward by integrating space syntax with the theory of the image of city. This entails the essential elucidation of spatial cognition pattern and image and the integration of space syntax and the image of city. The paper analyzes and demonstrates the prerequisite and feasibility of the mutual complementarily between space syntax and the image of city. The threedimensional space syntax integrated with the image of city is different from the traditional syntax that is only limited to non-figurative two-dimensional spatial cognition. It also emphasizes the important influences of the three-dimensional image points on people's behaviour in space and it is quite close to the accurate description of “natural movement”. Based on the three-dimensional theory, a concept model of the threedimensional syntax is constructed, and its content includes how to describe forming process of the three-dimensional spatial image pots during the cognition activity, how to construct the objective evaluation model for the three-dimensional image pots and how to put forward the concept of weighted-integration to calculate the concept mathematical model. In the end, the paper carries through three experimental analyses. The paper argues that the extended three-dimensional syntactical model is much closer to people's spatial illation and cognition. It should be more accurate to use the model to describe and grasp the urban configuration.
ISCEV Standard for full-field clinical electroretinography (2015 update)
(2014)

This document, from the International Society for Clinical Electrophysiology of Vision (ISCEV), presents an updated and revised ISCEV Standard for full-field clinical electroretinography (ffERG or simply ERG). The parameters for Standard flash stimuli have been revised to accommodate a variety of light sources including gas discharge lamps and light emitting diodes. This ISCEV Standard for clinical ERGs specifies six responses based on the adaptation state of the eye and the flash strength: (1) Dark-adapted 0.01 ERG (rod ERG); (2) Dark-adapted 3 ERG (combined rod-cone standard flash ERG); (3) Dark-adapted 3 oscillatory potentials; (4) Dark-adapted 10 ERG (strong flash ERG); (5) Light-adapted 3 ERG (standard flash “cone” ERG); and (6) Light-adapted 30 Hz flicker ERG. ISCEV encourages the use of additional ERG protocols for testing beyond this minimum standard for clinical ERGs.
Analysis and Design of Current-Fed Half-Bridge (C)(LC)–( LC) Resonant Topology for Inductive Wireless Power Transfer Application
(2017)

This paper proposes and analyzes a new power electronics circuit topology for wireless inductive power transfer application using current-fed half-bridge converter with CCL-LC resonant network. The major focus is analysis and implementation of a new current-fed resonant topology with current-sharing and voltage doubling features. Generally, inductive power transfer circuits with current fed converter use parallel CL resonant tank to transfer power effectively through air gap. However, in medium power application, this topology suffers from a major limitation of high voltage stress across the inverter semiconductor devices owing to high reactive power consumed by loosely coupled coil. In proposed topology, this is mitigated by adding a capacitor in series with the coil developing series-parallel CCL tank. The power flow is controlled through variable frequency modulation. Soft-switching of the devices is obtained irrespective of the load current. For grid-to-vehicle or solar-to-vehicle, the converter is analyzed and detailed design procedure is illustrated. Experimental results are presented to verify the analysis and demonstrate the performance.
A Comparative Study of the Differences in Family Communication among Young Urban Adult Consumers and the Development of their Materialistic Values : A Discriminant Analysis
(2013)

The study attempts to uncover the characteristics of materialism groups in Malaysia. It assesses the differences between materialism groups, i.e., low and high materialistic groups, using demographic and family communication dimensions. The data was collected through self-administered questionnaires. The sample consisted of 956 respondents. The majority of the respondents were Malays followed by Chinese and Indians. The proportion of female respondents was higher than the male respondents. Most of the respondents were single and in the age group of between 19-29 years old. Independent sample t-tests were used to compare mean scores for the study variables between ‘high’ materialism and ‘low’ materialism groups and significant mean differences were found between ‘high’ and ‘low’ materialism groups in terms of socio-oriented family communication construct. Specifically, it was found that the ‘high’ materialism group has considerably greater ratings on the constructs. Internal consistency reliability assessment using Cronbach coefficient alpha revealed that all the three dimensions had high reliability. A stepwise discriminant analysis performed on two family communication variables found that one variable was significant in differentiating the two materialism groups. The variable was socio-oriented family communication. The implications, significance and limitations of the study are discussed.
Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems
(2019)

We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ε fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ε approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ε approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain.
 Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ε fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary’s distribution, then we design an algorithm that achieves a 1−ε fraction of the weighted average of the optimal profit of each distribution the adversary picks.
 In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ε) the mixed packing-covering problem with O(γ m log (n/Δ)/ε2) oracle calls where the constraint matrix of this LP has dimension n× m, the success probability of the algorithm is 1−Δ, and γ quantifies how significant a single request is when compared to the sum total of all requests.
 We discuss implications of our results to several special cases including online combinatorial auctions, network routing, and the adwords problem.
Modeling wrinkles on smooth surfaces for footwear design
(2005)

We describe two new shape operators that superimpose wrinkles on top of a smooth NURBS surface. Previous research studying wrinkles focused mostly on cloth modeling or in animations, which are driven more by visual realism, but allow large elastic deformations. Our operators generate wrinkle-shaped deformations in a region of a smooth surface along a given boundary based on a few basic parametric inputs such as wrinkle magnitude and extent (these terms will be defined in the paper). The essential geometric transformation to map the smooth surface to a wrinkled one will be defined purely in terms of the geometry of the surface and the input parameters. Our model is based on two surface properties: geodesic offsets and surface energy. Practical implementation of the operators is discussed, and examples presented. Finally, the motivation for the operators will be given through their application in the computer-aided design and manufacture of footwear.
STREAM: A First Programming Process
(2009)

Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs.
 We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development.
 We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM.
 The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students’ process competences.
Clustering short text using Ncut-weighted non-negative matrix factorization
(2012)

Non-negative matrix factorization (NMF) has been successfully applied in document clustering. However, experiments on short texts, such as microblogs, Q&A documents and news titles, suggest unsatisfactory performance of NMF. An major reason is that the traditional term weighting schemes, like binary weight and tfidf, cannot well capture the terms' discriminative power and importance in short texts, due to the sparsity of data. To tackle this problem, we proposed a novel term weighting scheme for NMF, derived from the Normalized Cut (Ncut) problem on the term affinity graph. Different from idf, which emphasizes discriminability on document level, the Ncut weighting measures terms' discriminability on term level. Experiments on two data sets show our weighting scheme significantly boosts NMF's performance on short text clustering.
Vessel enhancement algorithm in digital retinal fundus microaneurysms filter for nonproliferative diabetic retinopathy classification
(2011)

Diabetic Retinopathy is one of a complication of diabetes which can cause blindness. There are two levels of diabetic retinopathy which are nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). The presence of microaneurysms in the eye is one of the early signs of diabetic retinopathy. Automated detection of microaneurysms in digital color fundus photographs is developed to help opthalmologist to detect the emergence of its early symptoms and determine the next action step for the patient. In this study we developed microaneurysms filter algorithm with the concept of vessel enhancement in order to extract the structure of microaneurysms in medical imagery, especially in the retinal image. Microaneurysms filter algorithm has been able to extract part of microaneurysms in the retinal image and detect pretty well. In addition, microaneurysms filters can also be developed to detect the next symptoms of diabetic retinopathy-hemorraghes (bleeding) that has features in common with microaneurysms which are larger and have irregular shape. Furthermore, the nonproliferative diabetic retinopathy (NPDR) classification system was developed based on the microaneurysms and hemorraghes detection. The system was tested against the patient data. Microanuerisyms detection results on DiaRetDB data has sensitivity value by 57,4% to ophthalmlogist 1, 35,37% for opthalmologist 2 and 62,68% for opthalmologist 3.
IRFs: master regulators of signalling by Toll-like receptors and cytosolic pattern-recognition receptors
(2006)

The interferon-regulatory factor (IRF) family of transcription factors was initially found to be involved in the induction of genes that encode type I interferons. IRFs have now been shown to have functionally diverse roles in the regulation of the immune system. Recently, the crucial involvement of IRFs in innate and adaptive immune responses has been gaining much attention, particularly with the discovery of their role in immunoregulation by Toll-like receptors and other pattern-recognition receptors.
A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data
(2015)

In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales. ∗Corresponding author Preprint submitted to Computers, Enviroment and Urban Systems September 10, 2014 ar X iv :1 40 9. 28 26 v1 [ cs .S I] 8 S ep 2 01 4
Speedy bus mastering PCI express
(2012)

PCI Express is a ubiquitous bus interface providing the highest bandwidth connection in the PC platform. Sadly, support for it in FPGAs is limited and/or expensive. The Speedy PCIe core addresses this problem by bridging the gap from the bare bones interface to a user friendly, high performance design. This paper describes some of the fundamental design challenges and how they were addressed as well as giving detailed results. The hardware and software source code are available for free download from [12].
Real-Time or Near Real-Time Persisting Daily Healthcare Data Into HDFS and ElasticSearch Index Inside a Big Data Platform
(2017)

Mayo Clinic (MC) healthcare generates a large number of HL7 V2 messages—0.7–1.1 million on weekends and 1.7–2.2 million on business days at present. With multiple RDBMS-based systems, such a large volume of HL7 messages still cannot be real-time or near-real-time stored, analyzed, and retrieved for enterprise-level clinic and nonclinic usage. To determine if Big Data technology coupled with ElasticSearch technology can satisfy MC daily healthcare needs for HL7 message processing, a BigData platform was developed to contain two identical Hadoop clusters (TDH1.3.2 version)—each containing an ElasticSearch cluster and instances of a storm topology—MayoTopology for processing HL7 messages on MC ESB queues into an ElasticSearch index and the HDFS. The implemented BigData platform can process 62 ± 4 million HL7 messages per day while the ElasticSearch index can provide ultrafast free-text searching at a speed level of 0.2-s per query on an index containing a dataset of 25 million HL7-derived-JSON-documents. The results suggest that the implemented BigData platform exceeds MC enterprise-level patient-care needs.
egoSlider: Visual Analysis of Egocentric Network Evolution
(2016)

Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.
Has the Internet become indispensable?
(2004)

As was empirically demonstrated in [3], “the adoption rate of the Internet has exceeded that of earlier mass communication technologies by several magnitudes,” making it an “irreversible” innovation. Studies have also shown that for the generation of U.S.-based youth that grew up with the Internet, it is gradually displacing television as their main source of entertainment, communication, and education [6]. Here, we explore how the Internet has become indispensable to people in their daily lives and develop a conceptual model allowing us to address the associated research questions. The idea is that the Internet has become so embedded in the daily fabric of people’s lives that they simply cannot live without it. How is the Internet indispensable and in what ways? For which groups of people is it indispensable, for what tasks, and how has BY DONNA L. HOFFMAN, THOMAS P. NOVAK, AND ALLADI VENKATESH
Improved Microaneurysm Detection using Deep Neural Networks
(2015)

In this work, we propose a novel microaneurysm (MA) detection for early dieabetic ratinopathy screening using color fundus images. Since MA usually the first lesions to appear as a indicator of diabetic retinopathy, accurate detection of MA is necessary for treatment. Each pixel of the image is classified as either MA or non-MA using deep neural network with dropout training procedure using maxout activation function. No preprocessing step or manual feature extraction is required. Substantial improvements over standard MA detection method based on pipeline of preprocessing, feature extraction, classification followed by postprocessing is achieved. The presented method is evaluated in publicly available Retinopathy Online Challenge (ROC) and Diaretdb1v2 database and achieved state-of-the-art accuracy.
Orthogonal nonnegative matrix t-factorizations for clustering
(2006)

Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.
Continuous deployment of software intensive products and services: A systematic mapping study
(2017)

BACKGROUND The software intensive industry is moving towards the adoption of a value-driven and adaptive real-time business paradigm. The traditional view of software as an item that evolves through releases every few months is being replaced by continuous evolution of software functionality. OBJECTIVE This study aims to classify and analyse literature related to continuous deployment in the software domain in order to scope the phenomenon, provide an overview of its state-of-the-art, investigate the scientific evidence in the reported results and identify areas that are suitable for further research. METHOD We conducted a systematic mapping study and classified the continuous deployment literature. The benefits and challenges related to continuous deployment were also analyzed. RESULTS The systematic mapping study includes 50 primary studies published between 2001 and 2014. An in-depth analysis of the primary studies revealed ten recurrent themes that characterize continuous deployment and provide researchers with directions for future work. In addition, a set of benefits and challenges of which practitioners may take advantage were identified. CONCLUSION Overall, although the topic area is very promising, it is still in its infancy, thus offering a plethora of new opportunities for both researchers and software intensive companies.